Deep Learning-Enhanced Iterative Modified Contrast Source Method for Electromagnetic Imaging in Half-Space

Wei-Tsong Lee, Chien-Ching Chiu (), Po-Hsiang Chen, Yen-Chun Li and Hao Jiang
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Wei-Tsong Lee: Department of Electrical and Computer and Engineering, Tamkang University, Tamsui 251301, Taiwan
Chien-Ching Chiu: Department of Electrical and Computer and Engineering, Tamkang University, Tamsui 251301, Taiwan
Po-Hsiang Chen: Department of Electrical and Computer and Engineering, Tamkang University, Tamsui 251301, Taiwan
Yen-Chun Li: Department of Electrical and Computer and Engineering, Tamkang University, Tamsui 251301, Taiwan
Hao Jiang: School of Engineering, San Francisco State University, San Francisco, CA 94117-1080, USA

Mathematics, 2025, vol. 13, issue 22, 1-38

Abstract: This paper presents a hybrid inversion framework that integrates a physics-informed iterative algorithm with a deep learning-based refinement strategy to address the electromagnetic inverse scattering problem of a uniaxial object buried in lossy half-space environments. Specifically, an Iterative Modified Contrast Scheme (IMCS) is developed to accelerate convergence and produce stable initial estimates, yielding improved performance compared to conventional contrast source methods. These estimates are subsequently refined by U-Net architecture, thereby enhancing the image quality of the reconstructed dielectric targets. Numerical simulations demonstrate that the proposed framework achieves robust and high-fidelity reconstructions of buried high-contrast dielectric objects, even in the presence of 20% additive Gaussian noise.

Keywords: Artificial Intelligence; transverse electric waves; iterative Modified Contrast Scheme; U-Net architecture; electromagnetic inverse scattering; half-space imaging (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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